IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics
Yunyao Zhang, Zuocheng Ying, Xinglang Zhang, Junqing Yu, Peng Fang, Xu Chen, Wei Yang, Zikai Song

TL;DR
IntervenSim is a novel simulation framework that models the continuous co-evolution of social events and interventions, improving accuracy and capturing opinion dynamics more effectively.
Contribution
It introduces an intervention-aware simulation framework that models event evolution and interventions in a closed loop, capturing dynamic social processes.
Findings
IntervenSim reduces MAPE by 41.6% and DTW by 66.9% compared to prior methods.
It achieves more faithful simulation of event trajectories and opinion dynamics.
Fewer agents are needed, lowering computational costs.
Abstract
LLM-based social network simulation introduces a new computational approach for modeling event evolution in complex online environments. However, existing methods typically simulate social processes under a fixed event trajectory, treating the event as static once initialized and overlooking intervention dynamics, and thus fail to capture the intrinsic evolution of real social network events, where source-side interventions and collective interactions continuously reshape event trajectories, sometimes leading to secondary popularity explosions and collective attitude shifts. To address this limitation, we introduce an intervention-aware simulation framework, IntervenSim, that models event evolution and intervention in a closed loop. We model event developments and source-side interventions using source agents, and collective crowd reactions using crowd agents, capturing their continuous…
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